Mini-BEHAVIOR-Gran: Revealing U-Shaped Effects of Instruction Granularity on Language-Guided Embodied Agents

• URL: http://arxiv.org/abs/2604.17019v1
• Date: Sat, 18 Apr 2026 14:57:51 GMT
• Title: Mini-BEHAVIOR-Gran: Revealing U-Shaped Effects of Instruction Granularity on Language-Guided Embodied Agents
• Abstract summary: Mini-BEHAVIOR-Gran is a new benchmark for controlled studies of instruction granularity in embodied AI.<n>We compare four candidate metrics for cross-task granularity quantification.<n>Using width to organize training and evaluation reveals a non-monotonic U-shaped relationship between instruction granularity and performance.
• Score: 58.64789157580261
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Instruction granularity is an important yet poorly controlled variable in language-guided embodied AI. Existing benchmarks typically pair each task with a single static instruction, making it difficult to study how agent behavior changes when the same task is described at different levels of detail. We introduce Mini-BEHAVIOR-Gran, a new benchmark for controlled studies of instruction granularity that extends Mini-BEHAVIOR with multiple instruction variants per task, ranging from high-level goal descriptions to step-by-step guidance. Using this benchmark, we compare four candidate metrics for cross-task granularity quantification: token count, entity count, action-verb count, and planning-width, and find that width correlates most consistently with agent performance. Using width to organize training and evaluation further reveals a non-monotonic U-shaped relationship between instruction granularity and performance, with peaks at both fine and coarse extremes. Further analysis suggests that the coarse-granularity performance rebound is associated with shallow grounding, where agents learn vision-dominant policies.

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